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Showing 1–8 of 8 results for author: Silva, M I

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  1. arXiv:2108.09200  [pdf, other

    cs.SI

    GUDIE: a flexible, user-defined method to extract subgraphs of interest from large graphs

    Authors: Maria Inês Silva, David Aparício, Beatriz Malveiro, João Tiago Ascensão, Pedro Bizarro

    Abstract: Large, dense, small-world networks often emerge from social phenomena, including financial networks, social media, or epidemiology. As networks grow in importance, it is often necessary to partition them into meaningful units of analysis. In this work, we propose GUDIE, a message-passing algorithm that extracts relevant context around seed nodes based on user-defined criteria. We design GUDIE for… ▽ More

    Submitted 20 August, 2021; originally announced August 2021.

    Comments: 16 pages, 8 figures, accepted at GEM2021

  2. arXiv:2108.04494  [pdf, other

    cs.SI

    Finding NeMo: Fishing in banking networks using network motifs

    Authors: Xavier Fontes, David Aparício, Maria Inês Silva, Beatriz Malveiro, João Tiago Ascensão, Pedro Bizarro

    Abstract: Banking fraud causes billion-dollar losses for banks worldwide. In fraud detection, graphs help understand complex transaction patterns and discovering new fraud schemes. This work explores graph patterns in a real-world transaction dataset by extracting and analyzing its network motifs. Since banking graphs are heterogeneous, we focus on heterogeneous network motifs. Additionally, we propose a no… ▽ More

    Submitted 10 August, 2021; originally announced August 2021.

    Comments: 6 pages, 6 figures, accepted at SEAData 2021

  3. arXiv:2105.14144  [pdf

    nlin.AO cs.CY

    A mechanism of Individualistic Indirect Reciprocity with internal and external dynamics

    Authors: Mario Ignacio González Silva, Ricardo Armando González Silva, Héctor Alfonso Juárez López, Antonio Aguilera Ontiveros

    Abstract: The cooperation mechanism of indirect reciprocity has been studied by making multiple variations of its parts. This research proposes a new variant of Nowak and Sigmund model, focused on agents' attitude; it is called Individualistic Indirect Reciprocity. In our model, an agent reinforces its strategy to the extent to which it makes a profit. We also include conditions related to the environment,… ▽ More

    Submitted 28 May, 2021; originally announced May 2021.

    Comments: 24 pages

    MSC Class: I.6

  4. arXiv:2102.05373  [pdf, other

    cs.LG cs.SI

    GuiltyWalker: Distance to illicit nodes in the Bitcoin network

    Authors: Catarina Oliveira, João Torres, Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro

    Abstract: Money laundering is a global phenomenon with wide-reaching social and economic consequences. Cryptocurrencies are particularly susceptible due to the lack of control by authorities and their anonymity. Thus, it is important to develop new techniques to detect and prevent illicit cryptocurrency transactions. In our work, we propose new features based on the structure of the graph and past labels to… ▽ More

    Submitted 21 July, 2021; v1 submitted 10 February, 2021; originally announced February 2021.

    Comments: 5 pages, 3 figures

  5. arXiv:2007.03727  [pdf, other

    cs.AI

    TripMD: Driving patterns investigation via Motif Analysis

    Authors: Maria Inês Silva, Roberto Henriques

    Abstract: Processing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy making. A common strategy to analyze driving behavior is to study the maneuvers being performance by the driver. In this paper, we propose TripMD, a system that extracts the most relevant driving patterns from senso… ▽ More

    Submitted 5 July, 2021; v1 submitted 7 July, 2020; originally announced July 2020.

    Comments: 14 pages, 11 figures, to be published in Expert Systems with Applications

  6. arXiv:2005.14635  [pdf, other

    cs.LG stat.ML

    Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity

    Authors: Joana Lorenz, Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro

    Abstract: Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking) harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms a… ▽ More

    Submitted 5 October, 2021; v1 submitted 29 May, 2020; originally announced May 2020.

    Comments: 8 pages, 7 figures

  7. arXiv:2004.08176  [pdf, other

    cs.LG stat.ML

    Exploring time-series motifs through DTW-SOM

    Authors: Maria Inês Silva, Roberto Henriques

    Abstract: Motif discovery is a fundamental step in data mining tasks for time-series data such as clustering, classification and anomaly detection. Even though many papers have addressed the problem of how to find motifs in time-series by proposing new motif discovery algorithms, not much work has been done on the exploration of the motifs extracted by these algorithms. In this paper, we argue that visually… ▽ More

    Submitted 17 April, 2020; originally announced April 2020.

    Comments: 8 pages, 12 figures, Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 2020

  8. arXiv:2002.04127  [pdf, other

    stat.ML cs.LG stat.AP

    Finding manoeuvre motifs in vehicle telematics

    Authors: Maria Inês Silva, Roberto Henriques

    Abstract: Driving behaviour has a great impact on road safety. A popular way of analysing driving behaviour is to move the focus to the manoeuvres as they give useful information about the driver who is performing them. In this paper, we investigate a new way of identifying manoeuvres from vehicle telematics data, through motif detection in time-series. We implement a modified version of the Extended Motif… ▽ More

    Submitted 10 February, 2020; originally announced February 2020.

    Comments: 11 pages, 3 figures, submitted to Accident Analysis & Prevention